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![Term Clustering into
Topics using LDA
❏ Tf*idf has evolved - a look at latent dirichlet
allocation [LDA]
❏ LDA is a generative statistical model
❏ Ability to generate probabilities; this is
where it gets interesting...
@nick_eubanks](https://image.slidesharecdn.com/tfidfandtheevolutionoftopicmodels1-180125191246/75/TF-IDF-and-the-Evolution-of-Topic-Models-19-2048.jpg)
Uploaded byNick Eubanks
TF*IDF and the Evolution of Topic Models
Nick Eubanks discusses the use of TF*IDF in topic modeling for SEO, emphasizing how it aids in understanding search engines and content relevance. He outlines the process of keyword research, topic identification, and content organization to drive sustainable and scalable web traffic. The document also covers advanced concepts like LDA for topic clustering and the importance of aligning web content with search engine expectations.
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TF*IDF and the Evolution of Topic Models
- 1. TF*IDF Introduction to Topic Modelingfor SEO 24 Hours of SEO Prepared for January 25, 2018 Nick Eubanks Presented by @nick_eubanks
- 2. Hi, I’m NickEubanks ❏ First business (painting) at 19 years old. $280k in revenue during summer of 2003. ❏ Launch first digital company in 2008 (atomni), built CMS for rapid deployment of Microsites. Sold. ❏ Build custom CMS for reviews in Japan. Angel funded. Sold. ❏ Built and sold lead-gen websites in medical, legal, and SEO. ❏ Joined TrafficSafetyStore.com in May 2012. Grew from ~$3M to well north of ~$20 Million ❏ Currently split time between NK Tech and IFTF. Nick Eubanks Founder and CEO, I’m From The Future Founder, TrafficThinkTank.com Executive Director, NK Tech Owner, ADDHero.com @nick_eubanks
- 3. We All WantMore Traffic to Our Website @nick_eubanks
- 4. Ideally traffic thatis... ❏ Sustainable ❏ Scalable ❏ Free @nick_eubanks
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- 6. Understanding Search Engines ❏Process of information retrieval; NLP ❏ Training machines to “understand” text ❏ Identifying patterns; topics ❏ Identifying importance; term frequency @nick_eubanks
- 7. In Comes TF*IDF ❏Term frequency x inverse document Frequency ❏ Process for machine-based information retrieval ❏ Calculates weights based on frequency of terms used throughout entire document set @nick_eubanks
- 8. What does thatlook like... ❏ Defining term “weights” based on the frequency of use through the document ❏ Removal of common “stop” words like the, and, is, because, and so on ❏ Excluding common HTML elements such as words used in header, footer, sidebars, and navigational elements @nick_eubanks
- 9. Topic Identification ❏ Identifythemes across terms to group into “topics” ❏ Ahrefs has some of this ❏ Much better with trained corpora using tf*idf ❏ Can do manually if you have the patience ❏ Use topics to design your content calendars @nick_eubanks
- 10. Building Relevance ❏ Process: Keywordresearch > Topic Identification > Theme ❏ Pillar content = Top-level theme ❏ Build topic content in clusters ❏ Example: https://www.hubspot.com/marketing -statistics @nick_eubanks
- 11. Imagine this Scenario ❏I gave you a stack of 1,000 documents ❏ Could you answer these questions: ❏ How would you organize them? ❏ What if I asked what they were about? ❏ What if I asked for all the documents about watermelons? ❏ How would you provide this information? @nick_eubanks
- 12. You would needto create a topic model ❏ To do this, you could use a set of different colored highlighters ❏ Then scan the documents looking for words that are related to each other, and highlight them ❏ Then you would create an index ❏ You would come up with a topic for each set of related terms ❏ You would annotate which pages terms relevant to that topic appeared on @nick_eubanks
- 13. This is HowSearch Engines Work @nick_eubanks
- 14. Example Topic: OrganicFarming ❏ Fruit ❏ Watermelons ❏ Lemons ❏ Apples ❏ Vegetables ❏ Squash ❏ Lettuce ❏ broccoli ❏ Organic foods ❏ usda ❏ Lower yields ❏ Organic standards ❏ Farmers ❏ Organic farmers ❏ Certified organic ❏ Synthetic pesticides @nick_eubanks
- 15. TF*IDF for “OrganicFarming” @nick_eubanks
- 16. Compared to #1Ranking URL @nick_eubanks
- 17. Probabilistic Complexities ❏ Automatingthe identification of topics ❏ Validating topics and training your model ❏ Creation of word vectors @nick_eubanks
- 18. What are WordVectors ❏ Simply, a vector of weights ❏ Word2Vec ❏ Pre-trained ❏ King - man + woman = queen ❏ Spatial relations where proximity represents relevance @nick_eubanks
- 19. Term Clustering into Topicsusing LDA ❏ Tf*idf has evolved - a look at latent dirichlet allocation [LDA] ❏ LDA is a generative statistical model ❏ Ability to generate probabilities; this is where it gets interesting... @nick_eubanks
- 21. Topic Clustering ❏ Groupingtopics into themes ❏ Where keyword research becomes important ❏ Building corpora of documents representative of relevant topics ❏ Scraping Google really helps with this @nick_eubanks
- 22. How Does ThisGet You More SEO Traffic? @nick_eubanks
- 23. What if youknew... ❏ The terms Google was expecting to find ❏ The topics Google was expecting to find ❏ The concepts that should be linked to ❏ The concepts and content that your page should be linked from? @nick_eubanks
- 24. Not “Keyword Density” ❏Dial in the term weights from tf*idf for a specific document ❏ Stuff in a bunch of keywords because you see them weighted heavily in the top ranking pages ❏ Google is much, much smarter than this ❏ Look for terms that represent topics ❏ Use those topics to perform additional keyword research ❏ Explore the pages currently ranking, and analyze their ❏ Internal links (both directions) ❏ External links (both directions) Don’t Do @nick_eubanks
- 25. Implementing Data froma Topic Model ❏ 28” orange traffic cones ❏ 18” lime traffic cones ❏ Portable traffic cones ❏ Yellow traffic cones ❏ Extendable traffic cone barricades Terms ❏ Orange traffic cones ❏ Lime traffic cones ❏ Colored traffic cones ❏ Collapsible traffic cones ❏ Cone bars ❏ Work zone safety Topics ❏ Traffic cones ❏ Safety cones ❏ Road safety cones Themes @nick_eubanks
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- 29. Looking at theURL Architecture ❏ Traffic Cones ❏ Orange and Lime Traffic Cones ❏ 28” Orange Traffic Cones ❏ 28” Lime Traffic Cones ❏ 18” Orange Traffic Cone ❏ Colored Traffic Cones ❏ Grabber Cones ❏ Cone Bars ❏ Collapsible Traffic Cones ❏ Traffic Cone Accessories Product Content ❏ Traffic Cones ❏ History of Traffic Cones ❏ Custom Traffic Cones ❏ Traffic Cone Selection Guide ❏ Traffic Cones for Construction @nick_eubanks
- 30. Implementing Data froma Topic Model ❏ Defining content type ❏ Designing content map Reference: imfromthefuture.com/content-map/ ❏ Laying out content calendar ❏ Designing an SEO-First URL Architecture ❏ Publishing ❏ Expanding keyword footprint Reference: imfromthefuture.com/bigfoot-strategy/ @nick_eubanks
- 31. Take TF*IDF Fora Spin https://imfromthefuture.com/tfidf-embed/ @nick_eubanks
- 32. Take LDA VisualizationFor a Spin https://imfromthefuture.com/lda-tool/ @nick_eubanks
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